import jittor as jt
from jittor import nn
from functools import partial
import os,sys
sys.path.append(os.getcwd())
from python.jseg.utils.helpers import to_2tuple
from python.jseg.utils.registry import BACKBONES
import math
from python.jseg.utils.weight_init import trunc_normal_init, normal_init, constant_init


class Mlp(nn.Module):
    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def execute(self, x, H, W):
        x = self.fc1(x)
        x = self.dwconv(x, H, W)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.,
                 sr_ratio=1):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim,
                                dim,
                                kernel_size=sr_ratio,
                                stride=sr_ratio)
            self.norm = nn.LayerNorm(dim)

    def execute(self, x, H, W):
        B, N, C = x.shape
        q = self.q(x).reshape(B, N, self.num_heads,
                              C // self.num_heads).permute(0, 2, 1, 3)

        if self.sr_ratio > 1:
            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
            x_ = self.norm(x_)
            kv = self.kv(x_).reshape(B, -1, 2, self.num_heads,
                                     C // self.num_heads).permute(
                                         2, 0, 3, 1, 4)
        else:
            kv = self.kv(x).reshape(B, -1, 2, self.num_heads,
                                    C // self.num_heads).permute(
                                        2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = nn.softmax(attn, dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class Block(nn.Module):
    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(dim,
                              num_heads=num_heads,
                              qkv_bias=qkv_bias,
                              qk_scale=qk_scale,
                              attn_drop=attn_drop,
                              proj_drop=drop,
                              sr_ratio=sr_ratio)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = nn.DropPath(
            drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim,
                       hidden_features=mlp_hidden_dim,
                       act_layer=act_layer,
                       drop=drop)

    def execute(self, x, H, W):
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.mlp(self.norm2(x), H, W))

        return x


class OverlapPatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """
    def __init__(self,
                 img_size=224,
                 patch_size=7,
                 stride=4,
                 in_chans=3,
                 embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)

        self.img_size = img_size
        self.patch_size = patch_size
        self.H, self.W = img_size[0] // patch_size[0], img_size[
            1] // patch_size[1]
        self.num_patches = self.H * self.W
        self.proj = nn.Conv2d(in_chans,
                              embed_dim,
                              kernel_size=patch_size,
                              stride=stride,
                              padding=(patch_size[0] // 2, patch_size[1] // 2))
        self.norm = nn.LayerNorm(embed_dim)

    def execute(self, x):
        x = self.proj(x)
        _, _, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = self.norm(x)

        return x, H, W


class MixVisionTransformer(nn.Module):
    def __init__(self,
                 img_size=224,
                 patch_size=16,
                 in_chans=3,
                 num_classes=1000,
                 embed_dims=[64, 128, 256, 512],
                 num_heads=[1, 2, 4, 8],
                 mlp_ratios=[4, 4, 4, 4],
                 qkv_bias=False,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 norm_layer=nn.LayerNorm,
                 depths=[3, 4, 6, 3],
                 sr_ratios=[8, 4, 2, 1]):
        super().__init__()
        self.num_classes = num_classes
        self.depths = depths

        # patch_embed
        self.patch_embed1 = OverlapPatchEmbed(img_size=img_size,
                                              patch_size=7,
                                              stride=4,
                                              in_chans=in_chans,
                                              embed_dim=embed_dims[0])
        self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4,
                                              patch_size=3,
                                              stride=2,
                                              in_chans=embed_dims[0],
                                              embed_dim=embed_dims[1])
        self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8,
                                              patch_size=3,
                                              stride=2,
                                              in_chans=embed_dims[1],
                                              embed_dim=embed_dims[2])
        self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16,
                                              patch_size=3,
                                              stride=2,
                                              in_chans=embed_dims[2],
                                              embed_dim=embed_dims[3])

        # transformer encoder
        dpr = [x.item() for x in jt.linspace(0, drop_path_rate, sum(depths))
               ]  # stochastic depth decay rule
        cur = 0
        self.block1 = nn.ModuleList([
            Block(dim=embed_dims[0],
                  num_heads=num_heads[0],
                  mlp_ratio=mlp_ratios[0],
                  qkv_bias=qkv_bias,
                  qk_scale=qk_scale,
                  drop=drop_rate,
                  attn_drop=attn_drop_rate,
                  drop_path=dpr[cur + i],
                  norm_layer=norm_layer,
                  sr_ratio=sr_ratios[0]) for i in range(depths[0])
        ])
        self.norm1 = norm_layer(embed_dims[0])

        cur += depths[0]
        self.block2 = nn.ModuleList([
            Block(dim=embed_dims[1],
                  num_heads=num_heads[1],
                  mlp_ratio=mlp_ratios[1],
                  qkv_bias=qkv_bias,
                  qk_scale=qk_scale,
                  drop=drop_rate,
                  attn_drop=attn_drop_rate,
                  drop_path=dpr[cur + i],
                  norm_layer=norm_layer,
                  sr_ratio=sr_ratios[1]) for i in range(depths[1])
        ])
        self.norm2 = norm_layer(embed_dims[1])

        cur += depths[1]
        self.block3 = nn.ModuleList([
            Block(dim=embed_dims[2],
                  num_heads=num_heads[2],
                  mlp_ratio=mlp_ratios[2],
                  qkv_bias=qkv_bias,
                  qk_scale=qk_scale,
                  drop=drop_rate,
                  attn_drop=attn_drop_rate,
                  drop_path=dpr[cur + i],
                  norm_layer=norm_layer,
                  sr_ratio=sr_ratios[2]) for i in range(depths[2])
        ])
        self.norm3 = norm_layer(embed_dims[2])

        cur += depths[2]
        self.block4 = nn.ModuleList([
            Block(dim=embed_dims[3],
                  num_heads=num_heads[3],
                  mlp_ratio=mlp_ratios[3],
                  qkv_bias=qkv_bias,
                  qk_scale=qk_scale,
                  drop=drop_rate,
                  attn_drop=attn_drop_rate,
                  drop_path=dpr[cur + i],
                  norm_layer=norm_layer,
                  sr_ratio=sr_ratios[3]) for i in range(depths[3])
        ])
        self.norm4 = norm_layer(embed_dims[3])

    def init_weights(self, pretrained=None):
        if pretrained is None:
            for m in self.modules():
                if isinstance(m, nn.Linear):
                    trunc_normal_init(m, std=.02, bias=0.)
                elif isinstance(m, nn.LayerNorm):
                    constant_init(m, val=1.0, bias=0.)
                elif isinstance(m, nn.Conv2d):
                    fan_out = m.kernel_size[0] * m.kernel_size[
                        1] * m.out_channels
                    fan_out //= m.groups
                    normal_init(m,
                                mean=0,
                                std=math.sqrt(2.0 / fan_out),
                                bias=0)
        elif isinstance(pretrained, str):
            self.load_parameters(jt.load(pretrained))

    def execute_features(self, x):
        B = x.shape[0]
        outs = []

        # stage 1
        x, H, W = self.patch_embed1(x)
        for i, blk in enumerate(self.block1):
            x = blk(x, H, W)
        x = self.norm1(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2)
        outs.append(x)

        # stage 2
        x, H, W = self.patch_embed2(x)
        for i, blk in enumerate(self.block2):
            x = blk(x, H, W)
        x = self.norm2(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2)
        outs.append(x)

        # stage 3
        x, H, W = self.patch_embed3(x)
        for i, blk in enumerate(self.block3):
            x = blk(x, H, W)
        x = self.norm3(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2)
        outs.append(x)

        # stage 4
        x, H, W = self.patch_embed4(x)
        for i, blk in enumerate(self.block4):
            x = blk(x, H, W)
        x = self.norm4(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2)
        outs.append(x)

        return outs

    def execute(self, x):
        x = self.execute_features(x)

        return x


class DWConv(nn.Module):
    def __init__(self, dim=768):
        super(DWConv, self).__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def execute(self, x, H, W):
        B, N, C = x.shape
        x = x.transpose(1, 2).view(B, C, H, W)
        x = self.dwconv(x)
        x = x.flatten(2).transpose(1, 2)

        return x


@BACKBONES.register_module()
class mit_b0(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b0, self).__init__(patch_size=4,
                                     embed_dims=[32, 64, 160, 256],
                                     num_heads=[1, 2, 5, 8],
                                     mlp_ratios=[4, 4, 4, 4],
                                     qkv_bias=True,
                                     norm_layer=partial(nn.LayerNorm,
                                                        eps=1e-6),
                                     depths=[2, 2, 2, 2],
                                     sr_ratios=[8, 4, 2, 1],
                                     drop_rate=0.0,
                                     drop_path_rate=0.1)


@BACKBONES.register_module()
class mit_b1(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b1, self).__init__(patch_size=4,
                                     embed_dims=[64, 128, 320, 512],
                                     num_heads=[1, 2, 5, 8],
                                     mlp_ratios=[4, 4, 4, 4],
                                     qkv_bias=True,
                                     norm_layer=partial(nn.LayerNorm,
                                                        eps=1e-6),
                                     depths=[2, 2, 2, 2],
                                     sr_ratios=[8, 4, 2, 1],
                                     drop_rate=0.0,
                                     drop_path_rate=0.1)


@BACKBONES.register_module()
class mit_b2(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b2, self).__init__(patch_size=4,
                                     embed_dims=[64, 128, 320, 512],
                                     num_heads=[1, 2, 5, 8],
                                     mlp_ratios=[4, 4, 4, 4],
                                     qkv_bias=True,
                                     norm_layer=partial(nn.LayerNorm,
                                                        eps=1e-6),
                                     depths=[3, 4, 6, 3],
                                     sr_ratios=[8, 4, 2, 1],
                                     drop_rate=0.0,
                                     drop_path_rate=0.1)


@BACKBONES.register_module()
class mit_b3(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b3, self).__init__(patch_size=4,
                                     embed_dims=[64, 128, 320, 512],
                                     num_heads=[1, 2, 5, 8],
                                     mlp_ratios=[4, 4, 4, 4],
                                     qkv_bias=True,
                                     norm_layer=partial(nn.LayerNorm,
                                                        eps=1e-6),
                                     depths=[3, 4, 18, 3],
                                     sr_ratios=[8, 4, 2, 1],
                                     drop_rate=0.0,
                                     drop_path_rate=0.1)


@BACKBONES.register_module()
class mit_b4(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b4, self).__init__(patch_size=4,
                                     embed_dims=[64, 128, 320, 512],
                                     num_heads=[1, 2, 5, 8],
                                     mlp_ratios=[4, 4, 4, 4],
                                     qkv_bias=True,
                                     norm_layer=partial(nn.LayerNorm,
                                                        eps=1e-6),
                                     depths=[3, 8, 27, 3],
                                     sr_ratios=[8, 4, 2, 1],
                                     drop_rate=0.0,
                                     drop_path_rate=0.1)


@BACKBONES.register_module()
class mit_b5(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b5, self).__init__(patch_size=4,
                                     embed_dims=[64, 128, 320, 512],
                                     num_heads=[1, 2, 5, 8],
                                     mlp_ratios=[4, 4, 4, 4],
                                     qkv_bias=True,
                                     norm_layer=partial(nn.LayerNorm,
                                                        eps=1e-6),
                                     depths=[3, 6, 40, 3],
                                     sr_ratios=[8, 4, 2, 1],
                                     drop_rate=0.0,
                                     drop_path_rate=0.1)
